def test_simple_back_and_forth_copy(self):
d = fwdpy11.DiscreteDemography([
fwdpy11.copy_individuals(1, 0, 1, 0.5),
fwdpy11.copy_individuals(2, 1, 0, 1.)
])
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 3)
md = np.array(self.pop.diploid_metadata)
deme_counts = np.unique(md['deme'], return_counts=True)
expected = {0: 150, 1: 50}
self.assertEqual(len(deme_counts[0]), len(expected))
for i in range(len(deme_counts[0])):
self.assertEqual(deme_counts[1][i], expected[i])
def test_copies_happen_before_moves(self):
"""
This is a more complex test.
In the same generation:
1. All individuals are copied from deme 0 to deme 1.
2. 50% of deme 0 moves to deme 2.
3. 50% of deme 0 moves to deme 3.
This, at the end, the size of deme 1 should be
the initial size and the size of demes 2 and 3
should be 0.5*initial size
"""
m = [
fwdpy11.move_individuals(0, 0, 3, 0.5),
fwdpy11.copy_individuals(0, 0, 1, 1),
fwdpy11.move_individuals(0, 0, 2, 0.5)
]
d = fwdpy11.DiscreteDemography(m)
N0 = self.pop.N
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 1)
md = np.array(self.pop.diploid_metadata)
expected = {0: 0, 1: N0, 2: N0 // 2, 3: N0 // 2}
dc = np.unique(md['deme'], return_counts=True)
for i, j in zip(dc[0], dc[1]):
self.assertEqual(expected[i], j)
def test_move_too_many_individuals_from_same_deme_with_copy_in_middle(
self):
# Attempt to move 125% of deme 0
# This is a test that input data get sorted so
# that copies are before moves, which will allow
# detecting the invalid cumulative move attempt
m = [
fwdpy11.move_individuals(0, 0, 1, 0.5),
fwdpy11.copy_individuals(0, 0, 1, 0.5),
fwdpy11.move_individuals(0, 0, 2, 0.75)
]
with self.assertRaises(ValueError):
fwdpy11.DiscreteDemography(m)
def test_init_with_one_mass_move_and_one_mass_copy_same_generation(self):
# Internally, the input data will get sorted
# so that copies happen before moves.
m = [
fwdpy11.move_individuals(0, 0, 1, 0.5),
fwdpy11.copy_individuals(0, 0, 1, 0.5)
]
try:
d = fwdpy11.DiscreteDemography(m)
self.assertEqual(d.mass_migrations[0].move_individuals, False)
self.assertEqual(d.mass_migrations[1].move_individuals, True)
except: # NOQA
self.fail("unexpected exception")
def test_copy(self):
m = fwdpy11.copy_individuals(0, 2, 1, 0.1)
self.assertEqual(m.when, 0)
self.assertEqual(m.source, 2)
self.assertEqual(m.destination, 1)
self.assertEqual(m.number, 0)
self.assertEqual(m.fraction, 0.1)
self.assertEqual(m.move_individuals, False)
mp = pickle.dumps(m, -1)
ump = pickle.loads(mp)
self.assertEqual(ump.when, 0)
self.assertEqual(ump.source, 2)
self.assertEqual(ump.destination, 1)
self.assertEqual(ump.number, 0)
self.assertEqual(ump.fraction, 0.1)
self.assertEqual(ump.move_individuals, False)
self.assertTrue(m == ump)
def test_simple_copies_from_multiple_demes(self):
# Set 1/2 the population to start in deme 1:
md = np.array(self.pop.diploid_metadata, copy=False)
md['deme'][self.pop.N // 2:] = 1
# In generation 0, we move 1/2 of deme 1 to
# deme 2, which creates a new deme:
d = fwdpy11.DiscreteDemography(
[fwdpy11.copy_individuals(0, 1, 2, 0.5)])
# Evolve for one generation:
ddr.DiscreteDemography_roundtrip(self.rng, self.pop, d, 1)
# We now expect 50, 50, and 25 individuals in
# demes 0, 1, and 2
md = np.array(self.pop.diploid_metadata)
deme_counts = np.unique(md['deme'], return_counts=True)
expected = {0: 50, 1: 50, 2: 25}
self.assertEqual(len(deme_counts[0]), len(expected))
for i in range(len(deme_counts[0])):
self.assertEqual(deme_counts[1][i], expected[i])
def test_init_with_two_mass_copies_same_generation(self):
m = [fwdpy11.copy_individuals(0, 0, 1, 0.5)] * 2
with self.assertRaises(ValueError):
fwdpy11.DiscreteDemography(m)
